Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment
Abstract Mining safety heavily depends on ventilation, which constitutes a significant portion of the energy costs in operations. Optimizing mine ventilation systems (MVSO) is crucial for minimizing this energy expenditure. However, current algorithms encounter challenges when applied to large-scale...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-11-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-024-01619-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571141278924800 |
|---|---|
| author | Wentian Shang Jinzhang Jia |
| author_facet | Wentian Shang Jinzhang Jia |
| author_sort | Wentian Shang |
| collection | DOAJ |
| description | Abstract Mining safety heavily depends on ventilation, which constitutes a significant portion of the energy costs in operations. Optimizing mine ventilation systems (MVSO) is crucial for minimizing this energy expenditure. However, current algorithms encounter challenges when applied to large-scale mines, primarily due to the complexity of variables and limited attention to optimizing main fans. This study introduces a theoretical knowledge enhanced genetic algorithm for MVSO, incorporating main fan adjustments. The algorithm models changes in the main fan’s operational status and integrates ventilation network equivalent simplification (VNES) and the minimum spanning tree (MST) to reduce the number of variables in the mine ventilation network. Additionally, leveraging mine ventilation sensitivity theory (MVST) enhances the quality of the initial algorithmic population. A simple case and two engineering cases collectively validated that the algorithm consistently provides effective and reliable optimization solutions for mine ventilation systems across varying scales. Specifically, the algorithm reduced energy consumption from 326.94 to 186.99 kW, 433.14 to 239.48 kW, and 520.53 to 324.90 kW across three different scales of mine ventilation systems. Comparative analysis with four other algorithms shows that, although this algorithm has a longer runtime due to the need to identify the minimum spanning tree during iterations, its ability to reduce problem dimensionality and improve population quality results in more stable and superior convergence performance, especially for large-scale mine ventilation systems. Graphical abstract |
| format | Article |
| id | doaj-art-01ca77cd82cd4a8687b63b6ad1fc5d13 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-01ca77cd82cd4a8687b63b6ad1fc5d132025-02-02T12:50:20ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111112110.1007/s40747-024-01619-5Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustmentWentian Shang0Jinzhang Jia1College of Safety Science and Engineering, Liaoning Technical UniversityCollege of Safety Science and Engineering, Liaoning Technical UniversityAbstract Mining safety heavily depends on ventilation, which constitutes a significant portion of the energy costs in operations. Optimizing mine ventilation systems (MVSO) is crucial for minimizing this energy expenditure. However, current algorithms encounter challenges when applied to large-scale mines, primarily due to the complexity of variables and limited attention to optimizing main fans. This study introduces a theoretical knowledge enhanced genetic algorithm for MVSO, incorporating main fan adjustments. The algorithm models changes in the main fan’s operational status and integrates ventilation network equivalent simplification (VNES) and the minimum spanning tree (MST) to reduce the number of variables in the mine ventilation network. Additionally, leveraging mine ventilation sensitivity theory (MVST) enhances the quality of the initial algorithmic population. A simple case and two engineering cases collectively validated that the algorithm consistently provides effective and reliable optimization solutions for mine ventilation systems across varying scales. Specifically, the algorithm reduced energy consumption from 326.94 to 186.99 kW, 433.14 to 239.48 kW, and 520.53 to 324.90 kW across three different scales of mine ventilation systems. Comparative analysis with four other algorithms shows that, although this algorithm has a longer runtime due to the need to identify the minimum spanning tree during iterations, its ability to reduce problem dimensionality and improve population quality results in more stable and superior convergence performance, especially for large-scale mine ventilation systems. Graphical abstracthttps://doi.org/10.1007/s40747-024-01619-5Mine ventilation system optimizationEnergy consumptionMain fanVentilation networkGenetic algorithm |
| spellingShingle | Wentian Shang Jinzhang Jia Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment Complex & Intelligent Systems Mine ventilation system optimization Energy consumption Main fan Ventilation network Genetic algorithm |
| title | Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment |
| title_full | Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment |
| title_fullStr | Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment |
| title_full_unstemmed | Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment |
| title_short | Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment |
| title_sort | theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment |
| topic | Mine ventilation system optimization Energy consumption Main fan Ventilation network Genetic algorithm |
| url | https://doi.org/10.1007/s40747-024-01619-5 |
| work_keys_str_mv | AT wentianshang theoreticalknowledgeenhancedgeneticalgorithmformineventilationsystemoptimizationconsideringmainfanadjustment AT jinzhangjia theoreticalknowledgeenhancedgeneticalgorithmformineventilationsystemoptimizationconsideringmainfanadjustment |